Prediction of missense mutation functionality depends on both the algorithm and sequence alignment employed

Elucidating the functional impact of G137V and G144R variants in Maroteaux Lamy's Syndrome by Molecular Dynamics Simulation

Mucopolysaccharidoses VI (Maroteaux Lamy syndrome) is a metabolic disorder due to the loss of enzyme activity of N-acetyl galactosamine-4-sulphatase arising from mutations in the ARSB gene. The mutated ARSB is the origin for the accumulation of GAGs within the lysosome leading to severe growth deformities, causing lysosomal storage disease. The main focus of this study is to identify the deleterious variants by applying bioinformatics tools to predict the conservation, pathogenicity, stability, and effect of the ARSB variants. We examined 170 missense variants, of which G137V and G144R were the resultant variants predicted detrimental to the progression of the disease. The native along with G137V and G144R structures were fixed as the receptors and subjected to Molecular docking with the small molecule Odiparcil to analyze the binding efficiency and the varied interactions of the receptors towards the drug. The interaction resulted in similar docking scores of - 7.3 kcal/mol indicating effective binding and consistent interactions of the drug with residues CYS117, GLN118, THR182, and GLN517 for native, along with G137V and G144R structures. Molecular Dynamics were conducted to validate the stability and flexibility of the native and variant structures on ligand binding. The overall study indicates that the drug has similar therapeutic towards the native and variant based on the higher binding affinity and also the complexes show stability with an average of 0.2 nm RMS value. This can aid in the future development therapeutics for the Maroteaux Lamy syndrome.

A computational approach to analyzing the functional and structural impacts of Tripeptidyl-Peptidase 1 missense mutations in neuronal ceroid lipofuscinosis

Neuronal ceroid-lipofuscinosis (NCLs) are a group of severe neurodegenerative conditions, most likely present in infantile, late infantile, juvenile, and adult-onset forms. Their phenotypic characteristics comprise eyesight damage, reduced motor activity and cognitive function, and sometimes tend to die in the initial stage. In recent studies, NCLs have been categorized into at least 14 genetic collections (CLN1-14). CLN2 gene encodes Tripeptidyl peptidase 1 (TPP1), which affects late infantile-onset form. In this study, we retrieved a mutational dataset screening for TPP1 protein from various databases (ClinVar, UniProt, HGMD). Fifty-six missense mutants were enumerated with computational methods to perceive the significant mutants (G475R and G501C) and correlated with clinical and literature data. A structure-based screening method was initiated to understand protein-ligand interaction and dynamic simulation. The docking procedure was performed for the native (3EDY) and mutant (G473R and G501C) structures with Gemfibrozil (gem), which lowers the lipid level, decreases the triglycerides amount in the blood circulation, and controls hyperlipidemia. The Native had an interaction score of -5.57 kcal/mol, and the mutants had respective average binding scores of -6.24 (G473R) and - 5.17 (G501C) kcal/mol. Finally, molecular dynamics simulation showed that G473R and G501C mutants had better flexible and stable orientation in all trajectory analyses. Therefore, this work gives an extended understanding of both functional and structural levels of influence for the mutant form that leads to NCL disorder.

Exploring the Role of Non-synonymous and Deleterious Variants Identified in Colorectal Cancer: A Multi-dimensional Computational Scrutiny of Exomes

Introduction

Colorectal cancers are the world's third most commonly diagnosed type of cancer. Currently, there are several diagnostic and treatment options to combat it. However, a delay in detection of the disease is life-threatening. Additionally, a thorough analysis of the exomes of cancers reveals potential variation data that can be used for early disease prognosis.

Methods

By utilizing a comprehensive computational investigation, the present study aimed to reveal mutations that could potentially predispose to colorectal cancer. Ten colorectal cancer exomes were retrieved. Quality control assessments were performed using FastQC and MultiQC, gapped alignment to the human reference genome (hg19) using Bowtie2 and calling the germline variants using Haplotype caller in the GATK pipeline. The variants were filtered and annotated using SIFT and PolyPhen2 successfully categorized the mutations into synonymous, non-synonymous, start loss and stop gain mutations as well as marked them as possibly damaging, probably damaging and benign. This mutational profile helped in shortlisting frequently occurring mutations and associated genes, for which the downstream multi-dimensional expression analyses were carried out.

Results

Our work involved prioritizing the non-synonymous, deleterious SNPs since these polymorphisms bring about a functional alteration to the phenotype. The top variations associated with their genes with the highest frequency of occurrence included LGALS8, CTSB, RAD17, CPNE1, OPRM1, SEMA4D, MUC4, PDE4DIP, ELN and ADRA1A. An in-depth multi-dimensional downstream analysis of all these genes in terms of gene expression profiling and analysis and differential gene expression with regard to various cancer types revealed CTSB and CPNE1 as highly expressed and overregulated genes in colorectal cancer.

Conclusion

Our work provides insights into the various alterations that might possibly lead to colorectal cancer and suggests the possibility of utilizing the most important genes identified for wet-lab experimentation.

Spectrum of variants associated with inherited retinal dystrophies in Northeast Mexico

BACKGROUND

Inherited retinal dystrophies are hereditary diseases which have in common the progressive degeneration of photoreceptors. They are a group of diseases with clinical, genetic, and allelic heterogeneity. There is limited information regarding the genetic landscape of inherited retinal diseases in Mexico, therefore, the present study was conducted in the northeast region of the country.

METHODS

Patients with inherited retinal dystrophies were included. A complete history, full ophthalmological and medical genetics evaluations, and genetic analysis through a targeted NGS panel for inherited retinal dystrophies comprising at least 293 genes were undertaken.

RESULTS

A total of 126 patients were included. Cases were solved in 74.6% of the study's population. Retinitis pigmentosa accounted for the most found inherited retinal disease. Ninety-nine causal variants were found, being USH2A and ABCA4 the most affected genes (26 and 15 cases, respectively).

CONCLUSIONS

The present study documents the most prevalent causative genes in IRDs, as USH2A, in northeastern Mexico. This contrasts with previous reports of IRDs in other zones of the country. Further studies, targeting previously unstudied populations in Mexico are important to document the genetic background of inherited retinal dystrophies in the country.

Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and relevance relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data is informative about how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models.

Perturbations in fatty acid metabolism and collagen production infer pathogenicity of a novel MBTPS2 variant in Osteogenesis imperfecta

Osteogenesis imperfecta (OI) is a heritable and chronically debilitating skeletal dysplasia. Patients with OI typically present with reduced bone mass, tendency for recurrent fractures, short stature and bowing deformities of the long bones. Mutations causative of OI have been identified in over 20 genes involved in collagen folding, posttranslational modification and processing, and in bone mineralization and osteoblast development. In 2016, we described the first X-linked recessive form of OI caused by MBTPS2 missense variants in patients with moderate to severe phenotypes. MBTPS2 encodes site-2 protease, a Golgi transmembrane protein that activates membrane-tethered transcription factors. These transcription factors regulate genes involved in lipid metabolism, bone and cartilage development, and ER stress response. The interpretation of genetic variants in MBTPS2 is complicated by the gene's pleiotropic properties; MBTPS2 variants can also cause the dermatological conditions Ichthyosis Follicularis, Atrichia and Photophobia (IFAP), Keratosis Follicularis Spinulosa Decalvans (KFSD) and Olmsted syndrome (OS) without skeletal abnormalities typical of OI. Using control and patient-derived fibroblasts, we previously identified gene expression signatures that distinguish MBTPS2-OI from MBTPS2-IFAP/KFSD and observed stronger suppression of genes involved in fatty acid metabolism in MBTPS2-OI than in MBTPS2-IFAP/KFSD; this was coupled with alterations in the relative abundance of fatty acids in MBTPS2-OI. Furthermore, we observed a reduction in collagen deposition in the extracellular matrix by MBTPS2-OI fibroblasts. Here, we extrapolate our observations in the molecular signature unique to MBTPS2-OI to infer the pathogenicity of a novel MBTPS2 c.516A>C (p.Glu172Asp) variant of unknown significance in a male proband. The pregnancy was terminated at gestational week 21 after ultrasound scans showed bowing of femurs and tibiae and shortening of long bones particularly of the lower extremity; these were further confirmed by autopsy. By performing transcriptional analyses, gas chromatography-tandem mass spectrometry-based quantification of fatty acids and immunocytochemistry on fibroblasts derived from the umbilical cord of the proband, we observed perturbations in fatty acid metabolism and collagen production similar to what we previously described in MBTPS2-OI. These findings support pathogenicity of the MBTPS2 variant p.Glu172Asp as OI-causative and highlights the value of extrapolating molecular signatures identified in multiomics studies to characterize novel genetic variants.

Assessing the relationship between the in silico predicted consequences of 97 missense mutations mapping to 68 genes related to lipid metabolism and their association with porcine fatness traits

In general, the relationship between the predicted functional consequences of missense mutations mapping to genes known to be involved in human diseases and the severity of disease manifestations is weak. In this study, we tested in pigs whether missense single nucleotide polymorphisms (SNPs), predicted to have consequences on the function of genes related to lipid metabolism are associated with lipid phenotypes. Association analysis demonstrated that nine out of 72 nominally associated SNPs were classified as "highly" or "very highly consistent" in silico-predicted functional mutations and did not show association with lipid traits expected to be affected by inactivation of the corresponding gene. Although the lack of endophenotypes and the limited sample size of certain genotypic classes might have limited to some extent the reach of the current study, our data indicate that present-day bioinformatic tools have a modest ability to predict the impact of missense mutations on complex phenotypes.

In silico assessment of missense point mutations on human cathelicidin LL-37

Cathelicidin antimicrobial peptides are a diverse family of cationic amphipathic peptides with multiple activities. In humans, cathelicidin LL-37 is one of the main host defense peptides with a remarkable medical and biotechnological potential. Deregulation of LL-37 expression has been associated with inflammatory diseases. However the effects of point mutations driven by single nucleotide polymorphisms (SNPs) on LL-37 are unknown. Here we applied an array of computational tools to investigate the effects of such mutations on LL-37 structure and activity. Due to the fact that, on cathelicidins, the prodomain is more conserved than the mature peptide, the SNP effect predictions were biased and, overall, resulted in neutral effects; and due to the slight changes in physicochemical properties, the antimicrobial predictions indicated the maintenance of such activity. Nonetheless, R07P, R07W, R29Q, R29W mutations reduced the peptide net charge, which in turn could result in less active LL-37 variants. Molecular dynamics data indicated that R07Q and N30Y mutations altered the LL-37 structure, leading to potential deleterious effects. In addition, the helix dipole is altered in G03A, R07P, R07W and L31P mutations, which could also alter the antimicrobial activity. Our results indicated that despite the mutations did not alter the residues from LL-37 active core, they could influence the antimicrobial activity and consequently, could be involved in inflammatory diseases.

Comprehensive In Silico Functional Prediction Analysis of CDKL5 by Single Amino Acid Substitution in the Catalytic Domain

Cyclin-dependent kinase-like 5 (CDKL5) is a serine/threonine protein kinase whose pathological mutations cause CDKL5 deficiency disorder. Most missense mutations are concentrated in the catalytic domain. Therefore, anticipating whether mutations in this region affect CDKL5 function is informative for clinical diagnosis. This study comprehensively predicted the pathogenicity of all 5700 missense substitutions in the catalytic domain of CDKL5 using in silico analysis and evaluating their accuracy. Each missense substitution was evaluated as “pathogenic” or “benign”. In silico tools PolyPhen-2 HumDiv mode/HumVar mode, PROVEAN, and SIFT were selected individually or in combination with one another to determine their performance using 36 previously reported mutations as a reference. Substitutions predicted as pathogenic were over 88.0% accurate using each of the three tools. The best performance score (accuracy, 97.2%; sensitivity, 100%; specificity, 66.7%; and Matthew’s correlation coefficient (MCC), 0.804) was achieved by combining PolyPhen-2 HumDiv, PolyPhen-2 HumVar, and PROVEAN. This provided comprehensive information that could accurately predict the pathogenicity of the disease, which might be used as an aid for clinical diagnosis.

Variant predictions in congenital adrenal hyperplasia caused by mutations in CYP21A2

CYP21A2 deficiency represents 95% of congenital adrenal hyperplasia (CAH) cases, a group of genetic disorders that affect steroid biosynthesis. The genetic and functional analysis provide critical tools to elucidate complex CAH cases. One of the most accessible tools to infer the pathogenicity of new variants is in silico prediction. Here, we analyzed the performance of in silico prediction tools to categorize missense single nucleotide variants (SNVs) of CYP21A2. SNVs of CYP21A2 characterized in vitro by functional assays were selected to assess the performance of online single and meta predictors. SNVs were tested separately or in combination with the related phenotype (severe or mild CAH form). In total, 103 SNVs of CYP21A2 (90 pathogenic and 13 neutral) were used to test the performance of 13 single-predictors and four meta-predictors. All SNVs associated with the severe phenotypes were well categorized by all tools, with an accuracy of between 0.69 (PredictSNP2) and 0.97 (CADD), and Matthews’ correlation coefficient (MCC) between 0.49 (PoredicSNP2) and 0.90 (CADD). However, SNVs related to the mild phenotype had more variation, with the accuracy between 0.47 (S3Ds&GO and MAPP) and 0.88 (CADD), and MCC between 0.18 (MAPP) and 0.71 (CADD). From our analysis, we identified four predictors of CYP21A2 variant pathogenicity with good performance, CADD, ConSurf, DANN, and PolyPhen2. These results can be used for future analysis to infer the impact of uncharacterized SNVs in CYP21A2.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Clinical characteristics and proteome modifications in two Charcot-Marie-Tooth families with the AARS1 Arg326Trp mutation

BACKGROUND

Aminoacyl tRNA-synthetases are ubiquitously-expressed enzymes that attach amino acids to their cognate tRNA molecules. Mutations in several genes encoding aminoacyl tRNA-synthetases, have been associated with peripheral neuropathy, i.e. AARS1, GARS1, HARS1, YARS1 and WARS1. The pathogenic mechanism underlying AARS1-related neuropathy is not known.

METHODS

From 2012 onward, all probands presenting at Telemark Hospital (Skien, Norway) with peripheral neuropathy were screened for variants in AARS1 using an "in-house" next-generation sequencing panel. DNA from patient's family members was examined by Sanger sequencing. Blood from affected family members and healthy controls were used for quantification of AARS1 mRNA and alanine. Proteomic analyses were conducted in peripheral blood mononuclear cells (PBMC) from four affected family members and five healthy controls.

RESULTS

Seventeen individuals in two Norwegian families affected by Charcot-Marie-Tooth disease (CMT) were characterized in this study. The heterozygous NM_001605.2:c.976C > T p.(Arg326Trp) AARS1 mutation was identified in ten affected family members. All living carriers had a mild to severe length-dependent sensorimotor neuropathy. Three deceased obligate carriers aged 74-98 were reported to be unaffected, but were not examined in the clinic. Proteomic studies in PBMC from four affected individuals suggest an effect on the immune system mediated by components of a systemic response to chronic injury and inflammation. Furthermore, altered expression of proteins linked to mitochondrial function/dysfunction was observed. Proteomic data are available via ProteomeXchange using identifier PXD023842.

CONCLUSION

This study describes clinical and neurophysiological features linked to the p.(Arg326Trp) variant of AARS1 in CMT-affected members of two Norwegian families. Proteomic analyses based on of PBMC from four CMT-affected individuals suggest that involvement of inflammation and mitochondrial dysfunction might contribute to AARS1 variant-associated peripheral neuropathy.

Clinical phenotype of adult-onset liver disease in patients with variants in ABCB4, ABCB11, and ATP8B1

Variants in ATP8B1, ABCB11, and ABCB4 underlie the most prevalent forms of progressive familial intrahepatic cholestasis. We aim to describe variants in these genes in a cohort of patients with adult‐onset liver disease, and explore a genotype–phenotype correlation. Patients with onset of liver disease aged above 18 who underwent sequencing of cholestasis genes for clinical purposes over a 5‐year period were identified. Bioinformatic analysis of variants was performed. Liver histology was evaluated in patients with variants. Of the 356 patients tested, at least one variant was identified in 101 (28.4%): 46 ABCB4, 35 ABCB11, and 28 ATP8B1. Patients with ABCB4 variants had chronic liver disease (71.7%) and pregnancy‐associated liver dysfunction (75%), with a younger age of onset in more severe genotypes (p = 0.046). ABCB11 variants presented with pregnancy‐associated liver dysfunction (82.4%) and acute/episodic cholestasis (40%), with no association between age of onset and genotype severity. ATP8B1 variants were associated with chronic liver disease (75%); however, they were commonly seen in patients with an alternate etiology of liver disease and variants were of low predicted pathogenicity. In adults with suspected genetic cholestasis, variants in cholestasis genes were frequently identified and were likely to contribute to the development of liver disease, particularly ABCB4 and ABCB11. Variants were often in heterozygous state, and they should no longer be considered recessive Mendelian traits. Sequencing cholestasis genes in selected patients with adult‐onset disease should be considered, with interpretation in close collaboration with histopathologists and geneticists.

Variants in cholestasis genes were found in patients with adult‐onset liver disease. ABCB4 variants are associated with chronic biliary disease, variants in ABCB11 are seen in patients with acute cholestasis, and the clinical significance of ATP8B1 variants is not clear.

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.

Comprehensive Characterization of the Coding and Non-Coding Single Nucleotide Polymorphisms in the Tumor Protein p63 (TP63) Gene Using In Silico Tools

Single nucleotide polymorphisms (SNPs) help to understand the phenotypic variations in humans. Genome-wide association studies (GWAS) have identified SNPs located in the tumor protein 63 (TP63) locus to be associated with the genetic susceptibility of cancers. However, there is a lack of in-depth characterization of the structural and functional impacts of the SNPs located at the TP63 gene. The current study was designed for the comprehensive characterization of the coding and non-coding SNPs in the human TP63 gene for their functional and structural significance. The functional and structural effects of the SNPs were investigated using a wide variety of computational tools and approaches, including molecular dynamics (MD) simulation. The deleterious impact of eight nonsynonymous SNPs (nsSNPs) affecting protein stability, structure, and functions was measured by using 13 bioinformatics tools. These eight nsSNPs are in highly conserved positions in protein and were predicted to decrease protein stability and have a deleterious impact on the TP63 protein function. Molecular docking analysis showed five nsSNPs to reduce the binding affinity of TP63 protein to DNA with significant results for three SNPs (R319H, G349E, and C347F). Further, MD simulations revealed the possible disruption of TP63 and DNA binding, hampering the essential protein function. PolymiRTS study found five non-coding SNPs in miRNA binding sites, and the GTEx portal recognized five eQTLs SNPs in single tissue of the lung, heart (LV), and cerebral hemisphere (brain). Characterized nsSNPs and non-coding SNPs will help researchers to focus on TP63 gene loci and ascertain their association with certain diseases.

An empirical pipeline for personalized diagnosis of Lafora disease mutations

Lafora disease (LD) is a fatal childhood dementia characterized by progressive myoclonic epilepsy manifesting in the teenage years, rapid neurological decline, and death typically within ten years of onset. Mutations in either EPM2A, encoding the glycogen phosphatase laforin, or EPM2B, encoding the E3 ligase malin, cause LD. Whole exome sequencing has revealed many EPM2A variants associated with late-onset or slower disease progression. We established an empirical pipeline for characterizing the functional consequences of laforin missense mutations in vitro using complementary biochemical approaches. Analysis of 26 mutations revealed distinct functional classes associated with different outcomes that were supported by clinical cases. For example, F321C and G279C mutations have attenuated functional defects and are associated with slow progression. This pipeline enabled rapid characterization and classification of newly identified EPM2A mutations, providing clinicians and researchers genetic information to guide treatment of LD patients.

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Lafora disease (LD) patients present with varying clinical progression

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LD missense mutations differentially affect laforin function

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An empirical in vitro pipeline is used to classify laforin missense mutations

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Patient progression can be predicted based on mutation class

A comprehensive in Silico analysis of the functional and structural impact of single nucleotide polymorphisms (SNPs) in the human IL-33 gene

Interleukin 33 (IL-33) is the latest member of the IL-1 cytokine family, which plays both pro - and anti-inflammatory functions. Numerous Single-nucleotide polymorphisms (SNPs) in the IL-33 gene have been recognized to be associated with a vast variety of inflammatory disorders. SNPs associated studies have become a crucial approach in uncovering the genetic background of human diseases. However, distinguishing the functional SNPs in a disease-related gene from a pool of both functional and neutral SNPs is a major challenge and needs multiple experiments of hundreds or thousands of SNPs in candidate genes. This study aimed to identify the possible deleterious SNPs in the IL-33 gene using bioinformatics predictive tools. The nonsynonymous SNPs (nsSNPs) were analyzed by SIFT, PolyPhen, PROVEAN, SNP&GO, MutPred, SNAP, PhD SNP, and I-Mutant tools. The Non-coding SNPs (ncSNPs) were also analyzed by SNPinfo and RegulomeDB tools. In conclusion, our in-silico analysis predicted 5 nsSNPs and 22 ncSNPs as potential candidates in the IL-33 gene for future genetic association studies.

Genomics pipelines to investigate susceptibility in whole genome and exome sequenced data for variant discovery, annotation, prediction and genotyping

Over the last few decades, genomics is leading toward audacious future, and has been changing our views about conducting biomedical research, studying diseases, and understanding diversity in our society across the human species. The whole genome and exome sequencing (WGS/WES) are two of the most popular next-generation sequencing (NGS) methodologies that are currently being used to detect genetic variations of clinical significance. Investigating WGS/WES data for the variant discovery and genotyping is based on the nexus of different data analytic applications. Although several bioinformatics applications have been developed, and many of those are freely available and published. Timely finding and interpreting genetic variants are still challenging tasks among diagnostic laboratories and clinicians. In this study, we are interested in understanding, evaluating, and reporting the current state of solutions available to process the NGS data of variable lengths and types for the identification of variants, alleles, and haplotypes. Residing within the scope, we consulted high quality peer reviewed literature published in last 10 years. We were focused on the standalone and networked bioinformatics applications proposed to efficiently process WGS and WES data, and support downstream analysis for gene-variant discovery, annotation, prediction, and interpretation. We have discussed our findings in this manuscript, which include but not are limited to the set of operations, workflow, data handling, involved tools, technologies and algorithms and limitations of the assessed applications.

In silico screening and molecular dynamics simulation of deleterious PAH mutations responsible for phenylketonuria genetic disorder

Phenylketonuria (PKU) is a genetic disorder that if left untreated can lead to behavioral problems, epilepsy, and even mental retardation. PKU results from mutations within the phenylalanine-4-hydroxylase (PAH) gene that encodes for the PAH protein. The study of all PAH causing mutations is improbable using experimental techniques. In this study, a collection of in silico resources, sorting intolerant from tolerant, Polyphen-2, PhD-SNP, and MutPred were used to identify possible pathogenetic and deleterious PAH non-synonymous single nucleotide polymorphisms (nsSNPs). We identified two variants of PAH, I65N and L311P, to be the most deleterious and disease causing nsSNPs. Molecular dynamics (MD) simulations were carried out to characterize these point mutations on the atomic level. MD simulations revealed increased flexibility and a decrease in the hydrogen bond network for both mutants compared to the native protein. Free energy calculations using the MM/GBSA approach found that BH4 , a drug-based therapy for PKU patients, had a higher binding affinity for I65N and L311P mutants compared to the wildtype protein. We also identify important residues in the BH4 binding pocket that may be of interest for the rational drug design of other PAH drug-based therapies. Lastly, free energy calculations also determined that the I65N mutation may impair the dimerization of the N-terminal regulatory domain of PAH.

Cooperative participation of epigenomic and genomic alterations in the clinicopathological diversity of gastric adenocarcinomas: significance of cell adhesion and epithelial-mesenchymal transition-related signaling pathways

The present study was conducted to clarify the cooperative significance of epigenomic and genomic abnormalities during gastric carcinogenesis. Using 21 samples of normal control gastric mucosa (C), 109 samples of non-cancerous gastric mucosa (N) and 105 samples of cancerous tissue (T) from 109 patients with primary gastric adenocarcinomas, genome-wide DNA methylation analysis was performed using Infinium assay. Among these samples, 66 paired N and corresponding T samples were subjected to whole-exome and single nucleotide polymorphism array analyses. As had been shown in our previous study, 109 patients were clustered clinicopathologically into least aggressive Cluster A (n = 20), most aggressive Cluster B1 (n = 20) and Cluster B2 (n = 69). Most DNA methylation alterations in each cluster had already occurred even in N samples compared with C samples, and DNA methylation alterations at the precancerous N stage were inherited by the established cancers themselves. Recurrent single nucleotide variants and insertions/deletions resulting in functional disruption of the proteins encoded by the ABCA10, BNC2, CDH1, CTNNB1, SMAD4 and VAV2 genes were specific to Cluster B1, whereas those of the APC, EGFR, ERBB2, ERBB3, MLH1 and MUC6 genes were specific to Cluster A. MetaCore pathway analysis revealed that the epigenomically affected TWIST1 gene and genomically affected CDH1, CTNNB1, MMP9, TLN2, ROCK1 and SMAD4 genes were accumulated in signaling pathways related to cell adhesion, cytoskeleton remodeling and epithelial–mesenchymal transition in Cluster B1. These data indicate that epigenomic alterations at the precancerous stage are important in gastric carcinogenesis and that epigenomic and genomic alterations cooperatively underlie the aggressiveness of gastric adenocarcinomas.

Structural effects driven by rare point mutations in amylin hormone, the type II diabetes-associated peptide

BACKGROUND

Amylin is a 37-amino-acid peptide hormone co-secreted with insulin, which participates in glucose homeostasis. This hormone is able to aggregate in a β-sheet conformation and deposit in islet amyloids, a hallmark in type II diabetes. Since amylin is a gene-encoded hormone, this peptide has variants caused by point mutations that can impact its functions.

METHODS

Here, we analyzed the structural effects caused by S20G and G33R point mutations which, according to the 1000 Genomes Project, have frequency in East Asian and European populations, respectively. The analyses were performed by means of aggrescan server, SNP functional effect predictors, and molecular dynamics.

RESULTS

We found that both mutations have aggregation potential and cause changes in the monomeric forms when compared with wild-type amylin. Furthermore, comparative analyses with pramlintide, an amylin drug analogue, allowed us to infer that second α-helix maintenance may be related to the aggregation potential.

CONCLUSIONS

The S20G mutation has been described as pathologically related, which is in agreement with our findings. In addition, our data suggest that the G33R mutation might have a deleterious effect. The data presented here also provide new therapy opportunities, whether for creating more effective drugs for diabetes or implementing specific treatment for patients with these mutations.

GENERAL SIGNIFICANCE

Our data could help to better understand the impact of mutations on the wild-type amylin sequence, as a starting point for the evaluation and characterization of other variations. Moreover, these findings could improve the health of patients with type II diabetes.

Protein modeling

Proteins are essential and versatile polymers consisting of sequenced amino acids that often possess an organized three-dimensional arrangement, (a result of their monomeric composition), which determines their biological role in cellular function. Proteins are involved in enzymatic catalysis; they participate in genetic information decoding and transmission processes, in cell recognition, in signaling, and transport of substances, in regulation of intra and extracellular conditions, and other functions.

TK, Ahmad MN, Okashah SS, Kamaraj B, Al-Subaie AM, C. GPD, Zayed H. Deciphering the Role of Filamin B Calponin-Homology Domain in Causing the Larsen Syndrome, Boomerang Dysplasia, and Atelosteogenesis Type I Spectrum Disorders via a Computational Approach

Filamins (FLN) are a family of actin-binding proteins involved in regulating the cytoskeleton and signaling phenomenon by developing a network with F-actin and FLN-binding partners. The FLN family comprises three conserved isoforms in mammals: FLNA, FLNB, and FLNC. FLNB is a multidomain monomer protein with domains containing an actin-binding N-terminal domain (ABD 1-242), encompassing two calponin-homology domains (assigned CH1 and CH2). Primary variants in FLNB mostly occur in the domain (CH2) and surrounding the hinge-1 region. The four autosomal dominant disorders that are associated with FLNB variants are Larsen syndrome, atelosteogenesis type I (AOI), atelosteogenesis type III (AOIII), and boomerang dysplasia (BD). Despite the intense clustering of FLNB variants contributing to the LS-AO-BD disorders, the genotype-phenotype correlation is still enigmatic. In silico prediction tools and molecular dynamics simulation (MDS) approaches have offered the potential for variant classification and pathogenicity predictions. We retrieved 285 FLNB missense variants from the UniProt, ClinVar, and HGMD databases in the current study. Of these, five and 39 variants were located in the CH1 and CH2 domains, respectively. These variants were subjected to various pathogenicity and stability prediction tools, evolutionary and conservation analyses, and biophysical and physicochemical properties analyses. Molecular dynamics simulation (MDS) was performed on the three candidate variants in the CH2 domain (W148R, F161C, and L171R) that were predicted to be the most pathogenic. The MDS analysis results showed that these three variants are highly compact compared to the native protein, suggesting that they could affect the protein on the structural and functional levels. The computational approach demonstrates the differences between the FLNB mutants and the wild type in a structural and functional context. Our findings expand our knowledge on the genotype-phenotype correlation in FLNB-related LS-AO-BD disorders on the molecular level, which may pave the way for optimizing drug therapy by integrating precision medicine.

Comprehensive in-silico analysis of damage associated SNPs in hOCT1 affecting Imatinib response in chronic myeloid leukemia

Non-synonymous single nucleotide polymorphisms (nsSNPs) in hOCT1 (encoded by SLC22A1 gene) are expected to affect Imatinib uptake in chronic myeloid leukemia (CML). In this study, sequence homology-based genetic analysis of a set of 270 coding SNPs identified 18 nsSNPs to be putatively damaging/deleterious using eight different algorithms. Subsequently, based on conservation of amino acid residues, stability analysis, posttranscriptional modifications, and solvent accessibility analysis, the possible structural-functional relationship was established for high-confidence nsSNPs. Furthermore, based on the modeling results, some dissimilarities of mutant type amino acids from wild-type amino acids such as size, charge, interaction and hydrophobicity were revealed. Three highly deleterious mutations consisting of P283L, G401S and R402G in SLC22A1 gene that may alter the protein structure, function and stability were identified. These results provide a filtered data to explore the effect of uncharacterized nsSNP and find their association with Imatinib resistance in CML.

Prediction and evaluation of deleterious and disease causing non-synonymous SNPs (nsSNPs) in human NF2 gene responsible for neurofibromatosis type 2 (NF2)

The majority of genetic variations in the human genome that lead to variety of different diseases are caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Neurofibromatosis type 2 (NF2) is a deadly disease caused by nsSNPs in the NF2 gene that encodes for a protein called merlin. This study used various in silico methods, SIFT, Polyphen-2, PhD-SNP and MutPred, to investigate the pathogenic effect of 14 nsSNPs in the merlin FERM domain. The G197C and L234R mutations were found to be two deleterious and disease mutations associated with the mild and severe forms of NF2, respectively. Molecular dynamics (MD) simulations were conducted to understand the stability, structure and dynamics of these mutations. Both mutant structures experienced larger flexibility compared to the wildtype. The L234R mutant suffered from more prominent structural instability, which may help to explain why it is associated with the more severe form of NF2. The intramolecular hydrogen bonding in L234R mutation decreased from the wildtype, while intermolecular hydrogen bonding of L234R mutation with solvent greatly increased. The native contacts were also found to be important. Protein-protein docking revealed that L234R mutation decreased the binding complementarity and binding affinity of LATS2 to merlin, which may have an impact on merlin's ability to regulate the Hippo signaling pathway. The calculated binding affinity of the LATS2 to L234R mutant and wildtype merlin protein is found to be 21.73 and -11 kcal/mol, respectively. The binding affinity of the wildtype merlin agreed very well with the experimental value, -8 kcal/mol.Communicated by Ramaswamy H. Sarma.

Non-random distribution of deleterious mutations in the DNA and protein-binding domains of IRF6 are associated with Van Der Woude syndrome

BACKGROUND

The development of the face occurs during the early days of intrauterine life by the formation of facial processes from the first Pharyngeal arch. Derangement in these well-organized fusion events results in Orofacial clefts (OFC). Van der Woude syndrome (VWS) is one of the most common causes of syndromic cleft lip and/or palate accounting for 2% of all cases. Mutations in the IRF6 gene account for 70% of cases with the majority of these mutations located in the DNA-binding (exon 3, 4) or protein-binding domains (exon 7-9). The current study was designed to update the list of IRF6 variants reported for VWS by compiling all the published mutations from 2013 to date as well as including the previously unreported VWS cases from Africa and Puerto Rico.

METHODS

We used PubMed with the search terms; "Van der Woude syndrome," "Popliteal pterygium syndrome," "IRF6," and "Orofacial cleft" to identify eligible studies. We compiled the CADD score for all the mutations to determine the percentage of deleterious variants.

RESULTS

Twenty-one new mutations were identified from nine papers. The majority of these mutations were in exon 4. Mutations in exon 3 and 4 had CADD scores between 20 and 30 and mutations in exon 7-9 had CADD scores between 30 and 40. The presence of higher CADD scores in the protein-binding domain (exon 7-9) further confirms the crucial role played by this domain in the function of IRF6. In the new cases, we identified five IRF6 mutations, three novel missense mutations (p.Phe36Tyr, p.Lys109Thr, and p.Gln438Leu), and two previously reported nonsense mutations (p.Ser424*and p.Arg250*).

CONCLUSION

Mutations in the protein and DNA-binding domains of IRF6 ranked among the top 0.1% and 1% most deleterious genetic mutations, respectively. Overall, these findings expand the range of VWS mutations and are important for diagnostic and counseling purposes.

Genotype-phenotype analysis of LMNA-related diseases predicts phenotype-selective alterations in lamin phosphorylation

Laminopathies are rare diseases associated with mutations in LMNA, which encodes nuclear lamin A/C. LMNA variants lead to diverse tissue-specific phenotypes including cardiomyopathy, lipodystrophy, myopathy, neuropathy, progeria, bone/skin disorders, and overlap syndromes. The mechanisms underlying these heterogeneous phenotypes remain poorly understood, although post-translational modifications, including phosphorylation, are postulated as regulators of lamin function. We catalogued all known lamin A/C human mutations and their associated phenotypes, and systematically examined the putative role of phosphorylation in laminopathies. In silico prediction of specific LMNA mutant-driven changes to lamin A phosphorylation and protein structure was performed using machine learning methods. Some of the predictions we generated were validated via assessment of ectopically expressed wild-type and mutant LMNA. Our findings indicate phenotype- and mutant-specific alterations in lamin phosphorylation, and that some changes in phosphorylation may occur independently of predicted changes in lamin protein structure. Therefore, therapeutic targeting of phosphorylation in the context of laminopathies will likely require mutant- and kinase-specific approaches.

Computational Analysis of nsSNPs of ADA Gene in Severe Combined Immunodeficiency Using Molecular Modeling and Dynamics Simulation

Severe combined immunodeficiency (SCID) is the most severe form of primary immunodeficiency (PID), characterized by fatal opportunistic infections. The ADA gene encodes adenosine deaminase, an enzyme that catalyzes the irreversible deamination of adenosine and deoxyadenosine in the catabolic pathway of purine. Mutations of the ADA gene have been identified in patients with severe combined immunodeficiency. In this study, we performed a bioinformatics analysis of the human ADA gene to identify potentially harmful nonsynonymous SNPs and their effect on protein structure and stability. Using eleven prediction tools, we identified 15 nsSNPs (H15D, H15P, H17Q, H17Y, D19N, T26I, G140E, C153F, A183D, G216R, H258Y, C262Y, S291L, S291W, and K34OE) as harmful. The results of ConSurf's analysis revealed that all these nsSNPs are localised in the highly conserved positions and affect the structure of the native proteins. In addition, our computational analysis showed that the H15D, G140E, G216R, and S291L mutations identified as being associated with severe combined immunodeficiency affect protein structure. Similarly, the results of the analyses of Rmsd, Rmsf, and Rg showed that all these factors influence protein stability, flexibility, and compaction with different levels of impact. This study is the first comprehensive computational analysis of nsSNPs of the ADA gene. However, functional analyses are needed to elucidate the biological mechanisms of these polymorphisms in severe combined immunodeficiency.

Consistency of the Tools That Predict the Impact of Single Nucleotide Variants (SNVs) on Gene Functionality: The BRCA1 Gene

Single nucleotide variants (SNVs) occurring in a protein coding gene may disrupt its function in multiple ways. Predicting this disruption has been recognized as an important problem in bioinformatics research. Many tools, hereafter p-tools, have been designed to perform these predictions and many of them are now of common use in scientific research, even in clinical applications. This highlights the importance of understanding the semantics of their outputs. To shed light on this issue, two questions are formulated, (i) do p-tools provide similar predictions? (inner consistency), and (ii) are these predictions consistent with the literature? (outer consistency). To answer these, six p-tools are evaluated with exhaustive SNV datasets from the BRCA1 gene. Two indices, called K a l l and K s t r o n g , are proposed to quantify the inner consistency of pairs of p-tools while the outer consistency is quantified by standard information retrieval metrics. While the inner consistency analysis reveals that most of the p-tools are not consistent with each other, the outer consistency analysis reveals they are characterized by a low prediction performance. Although this result highlights the need of improving the prediction performance of individual p-tools, the inner consistency results pave the way to the systematic design of truly diverse ensembles of p-tools that can overcome the limitations of individual members.

Comprehensive in silico screening and molecular dynamics studies of missense mutations in Sjogren-Larsson syndrome associated with the ALDH3A2 gene

Sjögren-Larsson syndrome (SLS) is an autoimmune disorder inherited in an autosomal recessive pattern. To date, 80 missense mutations have been identified in association with the Aldehyde Dehydrogenase 3 Family Member A2 (ALDH3A2) gene causing SLS. Disruption of the function of ALDH3A2 leads to excessive accumulation of fat in the cells, which interferes with the normal function of protective membranes or materials that are necessary for the body to function normally. We retrieved 54 missense mutations in the ALDH3A2 from the OMIM, UniProt, dbSNP, and HGMD databases that are known to cause SLS. These mutations were examined with various in silico stability tools, which predicted that the mutations p.S308N and p.R423H that are located at the protein-protein interaction domains are the most destabilizing. Furthermore, to determine the atomistic-level differences within the protein-protein interactions owing to mutations, we performed macromolecular simulation (MMS) using GROMACS to validate the motion patterns and dynamic behavior of the biological system. We found that both mutations (p.S380N and p.R423H) had significant effects on the protein-protein interaction and disrupted the dimeric interactions. The computational pipeline provided in this study helps to elucidate the potential structural and functional differences between the ALDH3A2 native and mutant homodimeric proteins, and will pave the way for drug discovery against specific targets in the SLS patients.

Refinement of evolutionary medicine predictions based on clinical evidence for the manifestations of Mendelian diseases

Prediction methods have become an integral part of biomedical and biotechnological research. However, their clinical interpretations are largely based on biochemical or molecular data, but not clinical data. Here, we focus on improving the reliability and clinical applicability of prediction algorithms. We assembled and curated two large non-overlapping large databases of clinical phenotypes. These phenotypes were caused by missense variations in 44 and 63 genes associated with Mendelian diseases. We used these databases to establish and validate the model, allowing us to improve the predictions obtained from EVmutation, SNAP2 and PoPMuSiC 2.1. The predictions of clinical effects suffered from a lack of specificity, which appears to be the common constraint of all recently used prediction methods, although predictions mediated by these methods are associated with nearly absolute sensitivity. We introduced evidence-based tailoring of the default settings of the prediction methods; this tailoring substantially improved the prediction outcomes. Additionally, the comparisons of the clinically observed and theoretical variations led to the identification of large previously unreported pools of variations that were under negative selection during molecular evolution. The evolutionary variation analysis approach described here is the first to enable the highly specific identification of likely disease-causing missense variations that have not yet been associated with any clinical phenotype.

Determining the pathogenicity of CFTR missense variants: Multiple comparisons of in silico predictors and variant annotation databases

Pathogenic variants in the Cystic Fibrosis Transmembrane Conductance Regulator gene (CFTR) are responsible for cystic fibrosis (CF), the commonest monogenic autosomal recessive disease, and CFTR-related disorders in infants and youth. Diagnosis of such diseases relies on clinical, functional, and molecular studies. To date, over 2,000 variants have been described on CFTR (~40% missense). Since few of them have confirmed pathogenicity, in silico analysis could help molecular diagnosis and genetic counseling. Here, the pathogenicity of 779 CFTR missense variants was predicted by consensus predictor PredictSNP and compared to annotations on CFTR2 and ClinVar. Sensitivity and specificity analysis was divided into modeling and validation phases using just variants annotated on CFTR2 and/or ClinVar that were not in the validation datasets of the analyzed predictors. After validation phase, MAPP and PhDSNP achieved maximum specificity but low sensitivity. Otherwise, SNAP had maximum sensitivity but null specificity. PredictSNP, PolyPhen-1, PolyPhen-2, SIFT, nsSNPAnalyzer had either low sensitivity or specificity, or both. Results showed that most predictors were not reliable when analyzing CFTR missense variants, ratifying the importance of clinical information when asserting the pathogenicity of CFTR missense variants. Our results should contribute to clarify decision making when classifying the pathogenicity of CFTR missense variants.

Clinical and molecular characterization of four patients with NTCP deficiency from two unrelated families harboring the novel SLC10A1 variant c.595A>C (p.Ser199Arg)

Sodium taurocholate cotransporting polypeptide (NTCP), a carrier protein encoded by solute carrier family 10 member 1 (SLC10A1), is expressed in the basolateral membrane of hepatocytes, where it is responsible for the uptake of bile acids from plasma into hepatocytes. The first patient with NTCP deficiency was described in 2015. A limited number of such patients have been reported in the literature and their genotypic and phenotypic features require further investigation. The current study investigated 4 patients with NTCP deficiency from two unrelated families. The patients were subjected to SLC10A1 genetic analysis and it was revealed that all patients were compound heterozygous for the c.800C>T (p.Ser267Phe) and c.595A>C (p.Ser199Arg) SLC10A1 variants. To the best of the authors' knowledge, the latter variant had not been previously reported. Further analysis in 50 healthy individuals did not identify carriers. The c.595A>C (p.Ser199Arg) variant exhibited co-segregation with hypercholanemia and exhibited a relatively conserved amino acid when compared with homologous peptides. Moreover, SWISS-MODEL prediction revealed that the mutation affected the conformation of the NTCP molecule. The 4 patients demonstrated varying degrees of hypercholanemia while a downward trend in the plasma levels of total bile acids (TBA) in 2 pediatric patients and occasionally normal TBA level in an adult case were observed. The results indicated an autosomal recessive trait for NTCP deficiency, supported the primary role of NTCP in the uptake of bile acids from plasma and suggested that hepatic uptake of bile acids may occur by means other than NTCP uptake. Moreover, the novel missense variant c.595A>C(p.Ser199Arg) enriched the SLC10A1 mutation spectrum and may serve as a new genetic marker for the molecular diagnosis and genetic counseling of NTCP deficiency.

Deep sequencing analysis to identify novel and rare variants in pain-related genes in patients with acute postoperative pain and high morphine use

Purpose

Most of the genetic variants that are reported to be associated with common pain phenotypes and analgesic use are common polymorphisms. The objective of our study was to identify new variants and investigate less common genetic variants that are usually not included in either small single-gene studies or high-throughput genotyping arrays.

Patients and methods

From a cohort of 1075 patients who underwent a scheduled total abdominal hysterectomy, 92 who had higher self-rated pain scores and used more morphine were selected for the re-sequencing of 105 genes.

Results

We identified over 2400 variants in 104 genes. Most were intronic with frequencies >5%. There were 181 novel variants, of which 30 were located in exons: 17 nonsynonymous, 10 synonymous, 2 non-coding RNA, and 1 stop-gain. For known variants that are rare (population frequency <1%), the frequencies of 54 exonic variants and eight intronic variants for the sequenced samples were higher than the weighted frequencies in the Genome Aggregation Database for East and South Asians (P-values ranging from 0.000 to 0.046). Overall, patients who had novel and/or rare variants used more morphine than those who only had common variants.

Conclusion

Our study uncovered novel variants in patients who reported higher pain and used more morphine. Compared with the general population, rare variants were more common in this group.

Computational analysis of high-risk SNPs in human CHK2 gene responsible for hereditary breast cancer: A functional and structural impact

Nowadays CHK2 mutation is studied frequently in hereditary breast and ovarian cancer patients in addition to BRCA1/BRCA2. CHK2 is a tumor suppressor gene that encodes a serine/threonine kinase, also involved in pathways such as DNA repair, cell cycle regulation and apoptosis in response to DNA damage. CHK2 is a well-studied moderate penetrance gene that correlates with third high risk susceptibility gene with an increased risk for breast cancer. Hence before planning large population study, it is better to scrutinize putative functional SNPs of CHK2 using different computational tools. In this study, we have used various computational approaches to identify nsSNPs which are deleterious to the structure and/or function of CHK2 protein that might be causing this disease. Computational analysis was performed by different in silico tools including SIFT, Align GVGD, SNAP-2, PROVEAN, Poly-Phen-2, PANTHER, PhD-SNP, MUpro, iPTREE-STAB, Consurf, InterPro, NCBI Conserved Domain Search tool, ModPred, SPARKS-X, RAMPAGE, Verify-3D, FT Site, COACH and PyMol. Out of 78 nsSNP of human CHK2 gene, seven nsSNPs were predicted functionally most significant SNPs. Among these seven nsSNP, p.Arg160Gly, p.Gly210Arg and p.Ser415Phe are highly conserved residues with conservation score of 9 and three nsSNP were predicted to be involved in post translational modification. The p.Arg160Gly and p.Gly210Arg may interfere in phosphopeptide binding site on FHA conserved domain. The p.Ser415Phe may interfere in formation of activation loop of protein-kinase domain and might interfere in interactions of CHK2 with ligand. The study concludes that mutation of serine to phenylalanine at position 415 is a major mutation in native CHK2 protein which might contribute to its malfunction, ultimately causing disease. This is the first comprehensive study, where CHK2 gene variants are analyzed using in silico tools hence it will be of great help while considering large scale studies and also in developing precision medicines related to these polymorphisms in the era of personalized medicine.

CAGI5: Objective performance assessments of predictions based on the Evolutionary Action equation

Many computational approaches estimate the effect of coding variants, but their predictions often disagree with each other. These contradictions confound users and raise questions regarding reliability. Performance assessments can indicate the expected accuracy for each method and highlight advantages and limitations. The Critical Assessment of Genome Interpretation (CAGI) community aims to organize objective and systematic assessments: They challenge predictors on unpublished experimental and clinical data and assign independent assessors to evaluate the submissions. We participated in CAGI experiments as predictors, using the Evolutionary Action (EA) method to estimate the fitness effect of coding mutations. EA is untrained, uses homology information, and relies on a formal equation: The fitness effect equals the functional sensitivity to residue changes multiplied by the magnitude of the substitution. In previous CAGI experiments (between 2011 and 2016), our submissions aimed to predict the protein activity of single mutants. In 2018 (CAGI5), we also submitted predictions regarding clinical associations, folding stability, and matching genomic data with phenotype. For all these diverse challenges, we used EA to predict the fitness effect of variants, adjusted to specifically address each question. Our submissions had consistently good performance, suggesting that EA predicts reliably the effects of genetic variants.

Structural prediction, whole exome sequencing and molecular dynamics simulation confirms p.G118D somatic mutation of PIK3CA as functionally important in breast cancer patients

To understand the structural and functional importance of PIK3CA somatic mutations, whole exome sequencing, molecular dynamics simulation techniques in combination with in silico prediction algorithms such as SIFT, PolyPhen, Provean and CADD were employed. Twenty out of eighty missense somatic mutations in PIK3CA gene were found to be pathogenic by all the four algorithms. Most recurrent mutations found were known hotspot PIK3CA mutations with known clinical significance like p.E545 K, p.E545A, p.E545 G and p.C420R. A missense mutation p.G118D was found to be recurrently mutated in 5 cases. Interestingly, this mutation was observed in one of the patients who underwent whole exome sequencing and was completely absent from the controls. To see the effect of this mutation on the structure of PIK3CA protein, molecular dynamics simulation was performed. By molecular dynamics approach, we have shown that p.G118D mutation deviated from the native structure which was supported by the decrease in the number of hydrogen bonds, difference in hydrogen bond distance and angle, difference in root mean square deviation between the native and the mutant structures.

Predicting mutations deleterious to function in beta-lactamase TEM1 using MM-GBSA

Missense mutations can have disastrous effects on the function of a protein. And as a result, they have been implicated in numerous diseases. However, the majority of missense variants only have a nominal impact on protein function. Thus, the ability to distinguish these two classes of missense mutations would greatly aid drug discovery efforts in target identification and validation as well as medical diagnosis. Monitoring the co-occurrence of a given missense mutation and a disease phenotype provides a pathway for classifying functionally disrupting missense mutations. But, the occurrence of a specific missense variant is often extremely rare making statistical links challenging to infer. In this study, we benchmark a physics-based approach for predicting changes in stability, MM-GBSA, and apply it to classifying mutations as functionally disrupting. A large and diverse dataset of 990 residue mutations in beta-lactamase TEM1 is used to assess performance as it is rich in both functionally disrupting mutations and functionally neutral/beneficial mutations. On this dataset, we compare the performance of MM-GBSA to alternative strategies for predicting functionally disrupting mutations. We observe that the MM-GBSA method obtains an area under the curve (AUC) of 0.75 on the entire dataset, outperforming all other predictors tested. More importantly, MM-GBSA’s performance is robust to various divisions of the dataset, speaking to the generality of the approach. Though there is one notable exception: Mutations on the surface of the protein are the mutations that are the most difficult to classify as functionally disrupting for all methods tested. This is likely due to the many mechanisms available to surface mutations to disrupt function, and thus provides a direction of focus for future studies.

Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene

In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and structure based drug design against SKP2.

Modeling mutant/wild-type interactions to ascertain pathogenicity of PROKR2 missense variants in patients with isolated GnRH deficiency

A major challenge in human genetics is the validation of pathogenicity of heterozygous missense variants. This problem is well-illustrated by PROKR2 variants associated with Isolated GnRH Deficiency (IGD). Homozygous, loss of function variants in PROKR2 was initially implicated in autosomal recessive IGD; however, most IGD-associated PROKR2 variants are heterozygous. Moreover, while IGD patient cohorts are enriched for PROKR2 missense variants similar rare variants are also found in normal individuals. To elucidate the pathogenic mechanisms distinguishing IGD-associated PROKR2 variants from rare variants in controls, we assessed 59 variants using three approaches: (i) in silico prediction, (ii) traditional in vitro functional assays across three signaling pathways with mutant-alone transfections, and (iii) modified in vitro assays with mutant and wild-type expression constructs co-transfected to model in vivo heterozygosity. We found that neither in silico analyses nor traditional in vitro assessments of mutants transfected alone could distinguish IGD variants from control variants. However, in vitro co-transfections revealed that 15/34 IGD variants caused loss-of-function (LoF), including 3 novel dominant-negatives, while only 4/25 control variants caused LoF. Surprisingly, 19 IGD-associated variants were benign or exhibited LoF that could be rescued by WT co-transfection. Overall, variants that were LoF in ≥ 2 signaling assays under co-transfection conditions were more likely to be disease-associated than benign or 'rescuable' variants. Our findings suggest that in vitro modeling of WT/Mutant interactions increases the resolution for identifying causal variants, uncovers novel dominant negative mutations, and provides new insights into the pathogenic mechanisms underlying heterozygous PROKR2 variants.

Comprehensive Computational Analysis of Protein Phenotype Changes Due to Plausible Deleterious Variants of Human SPTLC1 Gene

Genetic variations found in the coding and non-coding regions of a geneare known to influence the structure as well as the function of proteins. Serine palmitoyltransferase long chain subunit 1 a member of α-oxoamine synthase family is encoded by SPTLC1 gene which is a subunit of enzyme serine palmitoyltransferase (SPT). Mutations in SPTLC1 have been associated with hereditary sensory and autonomic neuropathy type I (HSAN-I). The exact mechanism through which these mutations elicit protein phenotype changes in terms of structure, stability and interaction with other molecules is unknown. Thus, we aimed to perform a comprehensive computational analysis of single nucleotide polymorphisms (SNPs) of SPTLC1 to prioritize a list of potential deleterious SNPs and to investigate the protein phenotype change due to functional polymorphisms. In this study, a diverse set of SPTLC1 SNPs were collected and scrutinized to categorize the potential deleterious variants. Our study concordantly identified 21 non- synonymous SNPs as pathogenic and deleterious that might induce alterations in protein structure, flexibility and stability. Moreover, evaluation of frameshift, 3’ and 5’ UTR variants shows c.*1302T> Gas effective. This comprehensive in silico analysis of systematically characterized list of potential deleterious variants could open avenues as primary filter to substantiate plausible pathogenic structural and functional impact of variants.

Identification of genetic variants associated with tacrolimus metabolism in kidney transplant recipients by extreme phenotype sampling and next generation sequencing

An extreme phenotype sampling (EPS) model with targeted next-generation sequencing (NGS) identified genetic variants associated with tacrolimus (Tac) metabolism in subjects from the Deterioration of Kidney Allograft Function (DeKAF) Genomics cohort which included 1,442 European Americans (EA) and 345 African Americans (AA). This study included 48 subjects separated into 4 groups of 12 (AA high, AA low, EA high, EA low). Groups were selected by the extreme phenotype of dose-normalized Tac trough concentrations after adjusting for common genetic variants and clinical factors. NGS spanned >3 Mb of 28 genes and identified 18,661 genetic variants (3,961 previously unknown). A group of 125 deleterious variants, by SIFT analysis, were associated with Tac troughs in EAs (burden test, p=0.008), CYB5R2 was associated with Tac troughs in AAs (SKAT, p=0.00079). In CYB5R2, rs61733057 (increased allele frequency in AAs) was predicted to disrupt protein function by SIFT and PolyPhen2 analysis. The variants merit further validation.

Comparative Genomics Approaches Accurately Predict Deleterious Variants in Plants

Recent advances in genome resequencing have led to increased interest in prediction of the functional consequences of genetic variants. Variants at phylogenetically conserved sites are of particular interest, because they are more likely than variants at phylogenetically variable sites to have deleterious effects on fitness and contribute to phenotypic variation. Numerous comparative genomic approaches have been developed to predict deleterious variants, but the approaches are nearly always assessed based on their ability to identify known disease-causing mutations in humans. Determining the accuracy of deleterious variant predictions in nonhuman species is important to understanding evolution, domestication, and potentially to improving crop quality and yield. To examine our ability to predict deleterious variants in plants we generated a curated database of 2,910 Arabidopsis thaliana mutants with known phenotypes. We evaluated seven approaches and found that while all performed well, their relative ranking differed from prior benchmarks in humans. We conclude that deleterious mutations can be reliably predicted in A. thaliana and likely other plant species, but that the relative performance of various approaches does not necessarily translate from one species to another.